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Fig. 7. Compounds 5a, 8b in Meroterpenoids with diverse structures and anti-inflammatory activities from Rhododendron anthopogonoides

Fig. 7. Compounds 5a, 8b, and 9 suppress the LPS-induced inflammatory responses in RAW 264.7 macrophages. A. Effects on the mRNA level of IL-1β. B. Effects on the mRNA level of IL-6. BAY 11–7082 (10 μM) was set as the positive control. Results are presented as mean ± SD (n = 3). *p <0.05, **p <0.01, ***p <0.001, ****p <0.001 vs the LPS group using one-way ANOVA.

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Fig. 5. Compounds 1–9 in (þ/¡)-Dievodialetins A¡G: Seven pairs of enantiomeric coumarin dimers with anti-acetylcholinesterase activity from the roots of Evodia lepta Merr.

Fig. 5. Compounds 1–9 ameliorate oxidative stress and neuroinflammation in scopolamine-treated SH-SY5Y cells.

Fig. 11. Superimposed structures for the docked compound 4 in Three phragmalin-type limonoids orthoesters and the structure of odoratone isolated from the bark of Entandrophragma candollei (Meliaceae)

Fig. 11. Superimposed structures for the docked compound 4 and VLB to the α and β subunit of tubulin. Compound 4 showed a different binding pocket at the interface between α and β subunit from the reference compound VLB.

Fig. 5 in Catecholic alkaloid sulfonates and aromatic nitro compounds from Portulaca oleracea and screening of their anti-inflammatory and anti-microbial activities

Fig. 5. Possible biosynthetic pathway for the nitro derivatives, catecholic alkaloids and their sulfonates from P. oleracea (DDC: dopa decarboxylase; RNS: reactive nitrogen species; TH: tyrosine hydroxylase).

Fig. 4. Compound 1 in Diverse alkaloids from the aerial parts of Aconitum carmichaelii and antiproliferative activity of costemline via inhibiting SIRT1/ROCK1/ P-STAT3 pathways

Fig. 4. Compound 1 inhibited invasion of HCT116 colon cancer cells in a transwell assay, which showed by representative images (A) and attached data (B). Cells were incubated with compound 1 (a) 0 μM, (b) 5 μM, (c) 10 μM for 24 h (original magnification, × 200). Quantitative data are presented as the mean ± standard deviation of 3 replicates, relative to the 0 μM control group. **p <0.01 vs. the 0 μM control group.

Fig. 6 in Catecholic alkaloid sulfonates and aromatic nitro compounds from Portulaca oleracea and screening of their anti-inflammatory and anti-microbial activities

Fig. 6. Dose-dependent inhibition of nitro derivative 12 against LPS-induced NO production in RAW 264.7 macrophage cells (n = 3) (****p <0.0001, versus vehicle control, ####p <0.0001, versus LPS-treated model, 3,4-dihydroxy-benzohydroxamic acid (Didox) was used as the positive control with IC value of 70 μM).

Fig. 4 in Catecholic alkaloid sulfonates and aromatic nitro compounds from Portulaca oleracea and screening of their anti-inflammatory and anti-microbial activities

Fig. 4. Calculated and experiment ECD of compounds 4, 10–11, 15–17 and their possible stereostructures

Fig. 1 in The metabolic sensitivity of hydrogen isotope fractionation differs between plant compounds

Fig. 1. Schematic, simplified overview of plant leaf carbohydrate, acetogenic lipid, and phytol synthesis. Reactions altered by impaired enzyme activities through mutations in the starch synthesis pathway marked in red for A. thaliana, or otherwise annotated for different species. Arrows in blue indicate putative reactions where C-bound H-exchange can occur. NADPH production is marked in purple and NADPH consumption is marked in pink. All used mutants accumulate trace amounts of starch, *except for the adg2-1 genotype, which accumulates around 40% of WT starch levels (Lin et al., 1988a). The MEP and acetogenic pathways are highlighted by yellow and orange dashed lines, respectively. Night-time starch degradation can provide a source of 2H-depleted carbohydrates in WT plants and is marked with brown dashed lines. Transport of G6P across the chloroplast membrane, indicated by the dashed black line, may occur when chloroplast G6P reach excess levels, like in pgm mutants (Kunz et al., 2010). Organic compounds extracted and measured for δ2H are shown in outlined white boxes. Abbreviations: 3PGA, 3-phosphoglycerate; Acetyl-CoA, acetyl coenzyme A; ADPGlc, ADP-glucose; CBB cycle, Calvin-Benson-Bassham cycle; DHAP, dihydroxyacetone phosphate; DOXP, 1-deoxy-D-xylulose 5-phosphate; F1,6BP, fructose 1,6-bisphosphate; F6P, fructose 6-phosphate; FA, fatty acid; Fd, ferredoxin; FNR, ferredoxin-NADP+ reductase; G6P, glucose 6-phosphate; G1P, glucose 1-phosphate; GAP, glyceraldehyde 3-phosphate; GGPP, geranylgeranyl pyrophosphate; Glc, glucose; IPP, isopentenyl pyrophosphate; Mal, maltose; MEP, 2-C-methylerythritol 4-phosphate; oxPPP, oxidative pentose phosphate pathway; Sucrose 6P, sucrose 6-phosphate; UDPGlc, UDP-glucose. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5 in The metabolic sensitivity of hydrogen isotope fractionation differs between plant compounds

Fig. 5. Schematic overview of the origin of H positions from phytol (A), n-C16:0 (B), and G6P and F6P (C). Red H represents that originating from RuBP and are annotated with possible positions in superscript, following nomenclature from Wieloch et al. (2022a): H1, H2, H6S and H6R, where H1 and H6S, and H2 and H6R are interchangeable at the triose-phosphate level, noted as H 1/6S and H 2/6R, respectively. Blue H represent those derived from surrounding water. Green H represent those derived from NADPH. Tallies of H atoms from specific sources are shown for different compounds. In these tallies, numbers of H1/6S are separately counted, highlighting that H 1/6S is contributing more H in phytol and carbohydrates compared to in acetogenic lipids. Superscript numbers on C atoms follow C positions 1 through 6 in hexose molecules. Although numbers on C atoms should also be considered interchangeable at the triose-phosphate level, we did not visualize this to maintain better traceability of C throughout reactions. Simplified visualization of RuBisCO carboxylation and oxygenation shows exchange of H 2/6R with water and 2H-enrichment of H1/6S in the photorespiration pathway, which end up in 3PGA. During the PGI reaction between F6P and G6P, H2/6R can transfer intramolecularly or H 2/6R can be exchanged with water, which should be considered if H 2/6R is thought to be an important driver for δ2 H values. Information compiled from different publications (Cormier et al., 2018; Eisenreich et al., 2004; Gutbrod et al., 2019; Lichtenthaler, 1999; Schleucher et al., 1999; Schmidt et al., 2003). Abbreviations: 3PGA, 3-phosphoglycerate; DMAPP, dimethylallyl pyrophosphate; DOXP, 1-deoxy-D-xylulose 5-phosphate; F6P, fructose 6-phosphate; G6P, glucose 6-phosphate; GAP, glyceraldehyde 3-phosphate; GGPP, geranylgeranyl pyrophosphate; IPP, isopentenyl pyrophosphate; MEP, 2-C-methyl-D-erythritol 4-phosphate; RuBP, ribulose 1,5-bisphosphate; TPP, thiamine pyrophosphate. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 in The metabolic sensitivity of hydrogen isotope fractionation differs between plant compounds

Fig. 3. Δεbio values of different compounds across starchless mutants of species with respect to WT mean value. Boxplot structure is explained in Fig. 2. Asterisks indicate a significant difference from zero (***p <0.001, **p <0.01, *p <0.05). Letters at the top of the plotting area indicate significant differences between the species within each respective compound (p <0.05). Letters below the plotting area indicate significant differences between compounds within a species (p <0.05), where species are separated by lines and fill colors, and letter positions are aligned with the respective mutant and compounds. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 in The metabolic sensitivity of hydrogen isotope fractionation differs between plant compounds

Fig. 4. Comparison of εbio values among species and mutants between phytol and n-C16:0 (A), and between phytol and cellulose (B). Dashed lines represent significant (p <0.05) correlations, with the black line representing the correlation across all species and genotypes, and the colored lines representing the correlations across genotypes within a species. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 12 in Natural sucrose esters: Perspectives on the chemical and physiological use of an under investigated chemical class of compounds

Fig. 12. NSEs extracted from Prunus padus, Smilax glabra, Bistorta manshuriensis and Aristolochia cretica.

Fig. 3 in Natural sucrose esters: Perspectives on the chemical and physiological use of an under investigated chemical class of compounds

Fig. 3. Examples of SEs synthesis. (A) heterogeneous catalysis approach and (B) total synthesis of phenylpropanoid SEs.

Fig. 8 in Natural sucrose esters: Perspectives on the chemical and physiological use of an under investigated chemical class of compounds

Fig. 8. Structure of NSEs extracted from Bidens parviflora, Phyllanthus cochinchinensis and Oryza sativa.

Fig. 2 in Natural sucrose esters: Perspectives on the chemical and physiological use of an under investigated chemical class of compounds

Fig. 2. Examples of SEs synthesis. (A) protection and unprotection approach and (B) biocatalysis approach.

Fig. 9 in Natural sucrose esters: Perspectives on the chemical and physiological use of an under investigated chemical class of compounds

Fig. 9. NSEs with phenylpropanoid substitutes extracted from Prunus tomentosa, Polygonum lapathifolium and Polygonum perfoliatum.

Fig. 4 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds

Fig. 4. Evaluation of searching blocks against those of BMDMS-NP. Blocks in (A) and (C) are from NIST17 and those in (B) and (D) are from MoNA. Lines marked with 'x' in (C) and (D) show the performance after application of precursor m/z filter had been adopted prior to calculation of the similarity score between blocks.

Fig. 3 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds

Fig. 3. Overview of BMDMS-NP. (A) The MS/MS library was established by two types of spectrometers with several conditions. (B) BMDMS-NP consists of information about compound spectra. The program provided by GitHub helped to search and build an in-house library. (C) Spectrum search is performed using the concept of data block. The spectra of the library are grouped as shown by the bold lines in the table. The user can group input spectral data into a block and search against BMDMS-NP and get ranking and scores for the searches.

Fig. 1 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds

Fig. 1. Principal component analysis of molecular fingerprints between the 'natural product/for sale' subset of ZINC and BMDMS-NP. The graph describes that the structural diversity of BMDMS-NP is overlapped with that of ZINC. In order to compare intuitively, the ZINC points were randomly selected to be the same quantity as the BMDMS-NP points and the 95% confidence interval ellipses are added.

Fig. 2 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds

Fig. 2. Venn diagram representing the number of overlapped metabolites between libraries. The number of each set was determined using InChIKey as the compound identifier.